Robust parallel decision-making in neural circuits with nonlinear inhibition
An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear w...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Proceedings of the National Academy of Sciences
2021
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| Online Access: | https://hdl.handle.net/1721.1/135223 |
| _version_ | 1811072954617823232 |
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| author | Kriener, Birgit Chaudhuri, Rishidev Fiete, Ila R |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Kriener, Birgit Chaudhuri, Rishidev Fiete, Ila R |
| author_sort | Kriener, Birgit |
| collection | MIT |
| description | An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear whether neurons can perform this max-finding operation in a way that improves upon the prohibitively slow optimal serial max-finding computation (which takes ~N log(N) time for N noisy candidate options) by a factor of N, the benchmark for parallel computation. Biologically plausible architectures for this task are winner-take-all (WTA) networks, where individual neurons inhibit each other so only those with the largest input remain active. We show that conventional WTA networks fail the parallelism benchmark and, worse, in the presence of noise, altogether fail to produce a winner when N is large. We introduce the nWTA network, in which neurons are equipped with a second nonlinearity that prevents weakly active neurons from contributing inhibition. Without parameter finetuning or rescaling as N varies, the nWTA network achieves the parallelism benchmark. The network reproduces experimentally observed phenomena like Hick's law without needing an additional readout stage or adaptive N-dependent thresholds. Our work bridges scales by linking cellular nonlinearities to circuitlevel decision-making, establishes that distributed computation saturating the parallelism benchmark is possible in networks of noisy, finite-memory neurons, and shows that Hick's law may be a symptom of near-optimal parallel decision-making with noisy input. |
| first_indexed | 2024-09-23T09:24:19Z |
| format | Article |
| id | mit-1721.1/135223 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:24:19Z |
| publishDate | 2021 |
| publisher | Proceedings of the National Academy of Sciences |
| record_format | dspace |
| spelling | mit-1721.1/1352232023-10-05T19:58:19Z Robust parallel decision-making in neural circuits with nonlinear inhibition Kriener, Birgit Chaudhuri, Rishidev Fiete, Ila R Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences McGovern Institute for Brain Research at MIT An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear whether neurons can perform this max-finding operation in a way that improves upon the prohibitively slow optimal serial max-finding computation (which takes ~N log(N) time for N noisy candidate options) by a factor of N, the benchmark for parallel computation. Biologically plausible architectures for this task are winner-take-all (WTA) networks, where individual neurons inhibit each other so only those with the largest input remain active. We show that conventional WTA networks fail the parallelism benchmark and, worse, in the presence of noise, altogether fail to produce a winner when N is large. We introduce the nWTA network, in which neurons are equipped with a second nonlinearity that prevents weakly active neurons from contributing inhibition. Without parameter finetuning or rescaling as N varies, the nWTA network achieves the parallelism benchmark. The network reproduces experimentally observed phenomena like Hick's law without needing an additional readout stage or adaptive N-dependent thresholds. Our work bridges scales by linking cellular nonlinearities to circuitlevel decision-making, establishes that distributed computation saturating the parallelism benchmark is possible in networks of noisy, finite-memory neurons, and shows that Hick's law may be a symptom of near-optimal parallel decision-making with noisy input. 2021-10-27T20:22:33Z 2021-10-27T20:22:33Z 2020 2021-03-19T14:11:06Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135223 en 10.1073/PNAS.1917551117 Proceedings of the National Academy of Sciences of the United States of America Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Proceedings of the National Academy of Sciences PNAS |
| spellingShingle | Kriener, Birgit Chaudhuri, Rishidev Fiete, Ila R Robust parallel decision-making in neural circuits with nonlinear inhibition |
| title | Robust parallel decision-making in neural circuits with nonlinear inhibition |
| title_full | Robust parallel decision-making in neural circuits with nonlinear inhibition |
| title_fullStr | Robust parallel decision-making in neural circuits with nonlinear inhibition |
| title_full_unstemmed | Robust parallel decision-making in neural circuits with nonlinear inhibition |
| title_short | Robust parallel decision-making in neural circuits with nonlinear inhibition |
| title_sort | robust parallel decision making in neural circuits with nonlinear inhibition |
| url | https://hdl.handle.net/1721.1/135223 |
| work_keys_str_mv | AT krienerbirgit robustparalleldecisionmakinginneuralcircuitswithnonlinearinhibition AT chaudhuririshidev robustparalleldecisionmakinginneuralcircuitswithnonlinearinhibition AT fieteilar robustparalleldecisionmakinginneuralcircuitswithnonlinearinhibition |